Book Image

Learning Predictive Analytics with Python

By : Ashish Kumar, Gary Dougan
Book Image

Learning Predictive Analytics with Python

By: Ashish Kumar, Gary Dougan

Overview of this book

Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form - It needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age. This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy. You’ll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world.
Table of Contents (19 chapters)
Learning Predictive Analytics with Python
Credits
Foreword
About the Author
Acknowledgments
About the Reviewer
www.PacktPub.com
Preface
A List of Links
Index

Chapter 5. Linear Regression with Python

If you have mastered the content of the last two chapters, implementing predictive models will be a cake walk. Remember the 80-20% split between the data cleaning + wrangling and modelling? Then what is the need of dedicating a full chapter to illustrate the model? The reason is not about running a predictive model; it is about understanding the mathematics (algorithms) that goes behind the ready-made methods which we will be using to implement these algorithms. It is about interpreting the swathe of results these models spew after the model implementation and making sense of them in the context. Thus, it is of utmost importance to understand the mathematics behind the algorithms and the result parameters of these models.

With this chapter onwards, we will deal with one predictive modelling algorithm in each chapter. In this chapter, we will discuss a technique called linear regression. It is the most basic and generic technique to create a predictive...